Performance Investigation of Learning Rate Decay in LMS-Based Equalization

Abstract

In this letter, we investigate the performances of different learning rate decay schedules in least mean square (LMS) based equalization. The required iteration numbers for convergence are 275, 150 and 150 for time-based, staircase and exponential decay schedules respectively, which are much less than 1000, the required iteration number for constant learning rate schedule in processing our experimental data. Meanwhile, the corresponding training times are reduced to shorter than one third, one fifth and one fourth of that in constant schedule. The system performances in the aspects of carrier to signal power ratio and optical signal to noise ratio are measured in a 110 Gbit/s probabilistically shaped 64 QAM direct detection system to evaluate different learning rate decay schedules.